Seurat analysis

The scRNA-seq dataset was processed and analyzed with Seurat version 2.3.4. Here, epithelial cell-derived fraction was identified from all dataset.

QC and selecting cells for further analysis

Scaling the data and removing unwanted sources of variation

## 
## Time Elapsed:  1.51834220091502 mins

Determine statistically significant principal components

## An object of class seurat in project RamDA-seq_EWF 
##  47667 genes across 1595 samples.

Finding differentially expressed features in minor populations

Cluster 4

Cluster 5

Cluster 6

Data visualization (ViolinPlot)

# Data visualization with ViolinPlot
my.data <- FetchData(EWF, c("ident", "nGene", "stage",
                            c("Cdh1", "Cdh3", "Krt5", "Krt14", "Epcam", "Itga6",
                              "Vim", "Col1a1", "Pdgfra", "Col3a1", "Fbn1",
                              "Kdr", "Flt4", "Dll4", "Pecam1", "Tek",
                              "Sox10", "Lgi4", "Gpr17", "Cdh19", "Plp1",
                              "Lilrb4a", "Coro1a", "Ccl3", "Lyz2", "Fcer1g",
                              "Kit", "Dct", "Mitf", "Pmel", "Mcoln3")))
my.data_melt <- reshape2::melt(my.data, id.vars = c("nGene", "stage", "ident"), variable.name = "genes", na.rm = TRUE)
my.data_melt$genes <- factor(my.data_melt$genes, 
                             levels = c("Cdh1", "Cdh3", "Krt5", "Krt14", "Epcam", "Itga6",
                                        "Vim", "Col1a1", "Pdgfra", "Col3a1", "Fbn1",
                                        "Kdr", "Flt4", "Dll4", "Pecam1", "Tek",
                                        "Sox10", "Lgi4", "Gpr17", "Cdh19", "Plp1",
                                        "Lilrb4a", "Coro1a", "Ccl3", "Lyz2", "Fcer1g",
                                        "Kit", "Dct", "Mitf", "Pmel", "Mcoln3"))
p5 <- ggplot(my.data_melt, aes(x = ident, y = value, fill = ident))
p5 <- p5 + geom_violin(scale = "width", adjust = 1, trim = TRUE) + coord_flip()
p5 <- p5 + scale_x_discrete(limits = c("Cluster6", "Cluster5", "Cluster4", "Cluster3", "Cluster2", "Cluster1"))
p5 <- p5 + theme(panel.background = element_rect(fill = "white"), 
                 strip.text.x = element_text(angle = 60, face = "italic", size = 15), 
                 axis.text.y = element_text(angle = 0, hjust = 1, face = "bold", colour = "black", size = 15), 
                 axis.text.x = element_blank(), 
                 axis.ticks.x = element_blank(), 
                 panel.spacing.x = unit(0, "lines"), 
                 panel.border = element_rect(color = "black", fill=NA, size = 0.5, linetype = 1), 
                 strip.background = element_rect(colour = NA, fill = NA)) + guides(fill = FALSE)
p5 <- p5 + facet_grid(. ~ genes, scales = "free", switch = "y")
p5 <- p5 + xlab("") + ylab("")
p5